Digital Storytelling Methods to Empower Young Black Adults in COVID-19 Vaccination Decision-Making: Feasibility Study and Demonstration
Bibliographic record
Abstract
BACKGROUND: Despite high rates of novel COVID-19, acceptance of COVID-19 vaccination is low among Black adults. In response, we developed a digital health intervention (Tough Talks-COVID) that includes digital stories created in a workshop we held with young Black adults. OBJECTIVE: Our formative research using digital storytelling workshops asked 3 research questions: (1) What issues did participants have in conceptualizing their stories, and what themes emerged from the stories they created? (2) What issues did participants have related to production techniques, and which techniques were utilized in stories? and (3) Overall, how did participants evaluate their workshop experience? METHODS: Participants were workshop-eligible if they were vaccine-accepting based on a baseline survey fielded in late 2021. Final participants (N=11) completed a consent process, all 3 workshops, and a media release form for their digital story. The first 2 workshops provided background information and hands-on digital storytelling skills from pre- to postproduction. The third workshop served as a screening and feedback session for participants' final videos. Qualitative and quantitative feedback elements were incorporated into all 3 sessions. RESULTS: Digital stories addressed one or more of 4 broad themes: (1) COVID-19 vulnerability, (2) community connections, (3) addressing vaccine hesitancy, and (4) countering vaccine misinformation. Participants incorporated an array of technical approaches, including unique creative elements such as cartoon images and instant messaging tools to convey social interactions around COVID-19 decision-making. Most (9/11, 82%) strongly agreed the digital storytelling workshops were delivered as expected; 10 of 11 agreed (n=5) or strongly agreed (n=5) that they had some ideas about what story to tell by the end of the first workshop, and most (8/11, 73%) strongly agreed they had narrowed down their ideas by workshop two. Of the participants, 9 felt they would very likely (n=6) or likely (n=3) use digital storytelling techniques for personal use in the future, and even more were very likely (n=7) to use the techniques for professional use. CONCLUSIONS: Our study is one of the first to incorporate digital storytelling as a central component to a digital health intervention and the only one to do so with exclusive focus on young Black adults. Our emphasis on digital storytelling was shown to be highly acceptable. Similar approaches, including careful consideration of the ethical challenges of community-based participatory approaches, are applicable to other populations experiencing both COVID-19 inequities and marginalization, such as other age demographics and people of color.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".